Inducements, Impediments, and Immediacy - Wiley Online Library

Journal of Small Business Management 2015 53(4), pp. 1011–1032 doi: 10.1111/jsbm.12113

Inducements, Impediments, and Immediacy: Exploring the Cognitive Drivers of Small Business Managers’ Intentions to Adopt Business Model Change by Oleksiy Osiyevskyy and Jim Dewald

Small business managers rely on judgment and heuristics when making critical strategic decisions. We explore this phenomenon, expanding the theory on cognition and strategy to explain the cognitive determinants of strategic decisions leading to small firm business model change. We integrate existing theories (entrepreneurial opportunity exploitation, cognitive resilience, prospect theory, behavioral theory of the firm, threat-rigidity) into a framework explaining strategic intentions, based on managers’ perception of business opportunity interacting with assessment of the external environment, current performance, and prior experience. The framework is empirically tested in the context of Canadian real estate brokerage industry, facing potentially major disruptive change.

Introduction Understanding how small business managers make strategic decisions and knowing what factors influence this process are of paramount importance for both researchers and practitioners. To date, researchers have devoted a disproportionate degree of attention to the study of strategic decision making in large corporations or start-ups (Gibcus, Vermeulen, and Radunova 2008; Ivanova and Gibcus 2003), largely ignoring specific features of established and operating small businesses whose managers face crucial decisions. The recent emergence of a research stream in entrepreneurship, concerned particularly with strategic decision making by small business managers acting as “established” entrepreneurs (Gibcus, Vermeulen, and Radunova 2008), is specifically aimed

at filling this gap. However, at this early stage of development, more research and refinement is needed. Scholars generally agree that the process of strategic decision making by small business managers differs from the process engaged by managers of large corporations (Busenitz and Barney 1997). Small businesses do not have the same level of time, resources (such as analysts), or information for making strategic decisions that would be classified as “rational.” Moreover, compared to their peers in large corporations, small businesses face higher uncertainty and instability when considering changes in the external environment (Tan 2001), and have to deal with complex nonlinear dynamical systems that produce hard to predict outcomes (Groves, Vance, and Choi 2011). Therefore, if compared with large companies, the process of

Oleksiy Osiyevskyy is PhD candidate in Strategy and Global Management at the Haskayne School of Business, University of Calgary. Jim Dewald is associate professor of Strategy and Global Management and the Dean at the Haskayne School of Business, University of Calgary. Address correspondence to: O. Osiyevskyy, Haskayne School of Business, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T3B 2V4. E-mail: [email protected].

OSIYEVSKYY AND DEWALD

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strategic decision-making in small firms is less rational (Levander and Raccuia 2001; Mador 2000), and relies heavily on heuristics and intuition (Busenitz and Barney 1997). Consequently, when studying strategic decision making by small firms, it is necessary to devote primary attention to the way individual ownermanagers perceive stimuli from the environment, process that information, and make and implement strategic decisions—in other words, the cognitive influence on strategic decision making. In this paper we elaborate on one of the basic questions in entrepreneurship research, about recognizing and exploiting opportunities (Baron 2004), by exploring the role of environmental stimuli as a driver for strategic decision making by small business managers. Particularly, we demonstrate that not all recognized opportunities yield decisions to act, and that the strategic decisions are influenced by other stimuli (such as threat, urgency, lack of satisfaction with current performance, or prior experience of implementing risky decisions), besides perceived opportunity. Drawing upon existing established theories from the related fields of organization theory and strategic management, we develop a theoretical framework embracing key cognitive factors that drive the small business manager’s strategic decision-making process. In particular, we focus on the decision to adopt changes in the firm’s business model, as a response to industry-level disruptive change. Using the recently proposed practical definition of a business model as three-dimensional system (George and Bock 2011), consisting of interrelated resource, transactive, and value structures, we relate cognitive determinants for change to each dimension individually. The framework is tested in the context of Canadian real estate brokerage industry, during a period of salient critical threat caused by discontinuous environmental turbulence and emergence of disruptive innovative business models. The empirical results support the hypothesized associations between cognitive stimuli and strategic decisions. There are three main contributions from this research. We advance the research stream on opportunity exploitation by developing and testing a cohesive theoretical framework that captures the cognitive prerequisites of the manager’s strategic decision making. Second, by employing the definition of a business model as

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three-dimensional structure (George and Bock 2011), we address the authors’ urge to propose “a cognitive model linking opportunity landscape assessment to business model design” (p. 106). Finally, we develop, test, and validate new composite scales for capturing cognitionbased constructs in survey research.

Cognitive Framing: Inducements, Impediments, and Time Pressure 3-Dimensional View of a Business Model In order to embrace a myriad of possible strategic decisions, we adopt the 3-dimensional view of business models proposed by George and Bock (2011). The first dimension, resource structure, defines the business’s tangible and intangible resource endowment, as well as the configuration of system coordinating usage of valuebearing resources to support business activities and value creation. Therefore, intended changes in resource structure result from strategic decisions to acquire new resources (e.g., buying equipment, developing software, hiring personnel), develop existing capabilities (e.g., training personnel), or divest unneeded resources and capabilities. The second dimension of a business model, transactive structure, determines the configuration and characteristics of intra-organizational and boundary-spanning transactions. Answering the question “how,” this dimension of business model defines the way operations (business processes) are organized to utilize available resources toward delivering value to stakeholders, including customers, partners, and owners. Intentions to change the business’s transactive structure are manifested through strategic decisions to alter the scope of transactions, their elements, characteristics, nature or efficiency. Finally, the value structure of a business model, answering the “what” question, determines the sources for value creation, as well as the mechanism of sharing the created value among the business’s stakeholders. Emphasizing the importance of this dimension, Becerra (2009) asserts that it is value creation that forms the core of theory of the firm from strategic management perspective. The value structure defines specifically how the business opportunity is enacted through resource and transactive configurations, and as

JOURNAL OF SMALL BUSINESS MANAGEMENT

such represents a higher order of change. Intended changes in value structure usually reveal themselves in decisions to reconfigure the mechanisms of sharing the value among stakeholders (e.g., decreasing margin to transfer value from the firm to its customers), or capturing additional value through enacting new opportunities in the environment. Adoption of a new business model could be categorized as either transformative or incremental. Transformative strategic change is represented by a change in value structure, with consequential changes to both transactive and resource structures. On the other hand, some small business managers will engage in incremental change, limited to “patching” a component or improving onto an existing business model in response to new environmental conditions. Whereas transformative change is seen in the value structure, incremental patches occur in only the resource structure or possibly the transactive structure.

The Role of Perceived Opportunity Opportunity was listed as one of the three possible stimuli for making strategic decisions in organizations (Mintzberg, Raisinghani, and Theoret 1976), and an important enabler for entrepreneurial actions (Drucker 1985). Since that time perceiving opportunities has become one of the main threads of entrepreneurship literature (Hansen, Shrader, and Monroll 2011; Kontinen and Ojala 2011; Lumpkin 2011; Mitchell et al. 2004), and is considered to be the primary driver for entrepreneurs’ actions (Shane and Venkataraman 2000). The positive association of perceived opportunity with intentions or actual adoption of a new business model was theoretically explored by Christensen and Raynor (2003) and Markides (2006). The empirical study by Dewald and Bowen (2010) demonstrated the significance of the association in the small business context. The qualitative study of entrepreneur’s decision making by Lucas, Vermeulen, and Curseu (2008) reveals that seeing the opportunity is a motive for making strategic decisions. Lang, Calantone, and Gudmundson (1997) found a positive association between opportunity perception and information seeking by small firms, an antecedent for further actions. Hence, in the case of strategic decisions made in reaction to industry’s disruptive innovation, we anticipate emergence of intentions

to change the business model as a response to incumbent’s perceived opportunity: H1: Perceived opportunity is positively associated with intention to change the firm’s business model.

Performance Shortfall, Non-Critical Threat, and Critical Threat Even though the dimensions of an alternative business model may be distinct and objectively defined, managerial perceptions of the same potential change are anything but uniform, and may vary from perceiving a opportunity for growth to a threat for survival. Moreover, these different cognitive stimuli can be perceived simultaneously, interacting with each other. For instance, Dewald and Bowen (2010) found that managers who are able to perceive both opportunity and threat simultaneously demonstrate a level of cognitive resilience that can lead to competitive advantage. Hence, although seemingly opposite to opportunity, threat can also lead to strategic decision making related to business model change. Research in the behavioral theory of the firm tradition (Cyert and March 1963) stresses that managers are more responsive to perceived threat rather than perceived opportunity. Similarly, in their seminal article on managerial decision process, Mintzberg, Raisinghani, and Theoret (1976) include “crises” in the list of decision-making stimuli, and by applying cognitive categorization theory Dutton and Jackson (1987) found that labeling a strategic issue as a threat leads to extreme response such as “taking actions of large magnitude” (p. 84). Gilbert and Bower (2002) note that when facing a disruptive change, industry incumbents who see it as a threat overreact by “committing too many resources too quickly” (p. 95). However, not all threats are the same, nor do they have the same influence on decision-makers (Shimizu 2007). Laughhunn, Payne, and Crum (1980), in their experiments involving 237 managers in multiple industries, found that 64 percent of their sample shift from predominantly riskseeking (for below target returns) to riskadverse when facing “ruinous” losses. These findings were a precursor to the March and Shapira (1987) two reference point model of managerial risk taking, wherein risk-seeking responses are most closely associated with lesser-impact, or incremental threat, and risk


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adverse or severe cautionary responses were more closely linked to large-scale, ruinous threat. Shimizu (2007) summarizes that Prospect Theory (Kahneman and Tversky 1979) and the Behavioral Theory of the Firm (Cyert and March 1963) are effective predictors of predominantly risk-seeking managerial response to “short-term, incremental, adjustments to relatively small performance gaps,” whereas the threat rigidity thesis more accurately predicts risk-adverse response to the threat of large losses (p. 1497). We delineate the perception of threat into three distinct variables: threat in the form of current (or recent past) performance being below aspirations (termed “performance shortfall”), threat that future performance will result in losses relative to a pre-set reference point (termed “non-critical threat”), and threat to future survival of the enterprise (termed “critical threat”). As an objective measure of past or current activities, performance shortfall is clearly distinct from the more subjective, perception-based, and future oriented categories of non-critical and critical threat. Although others contend that non-critical and critical threat represent one variable with two reference points (Hu, Blettner, and Bettis 2011; Shimizu 2007), the origin and fundamental characteristic of each classification of threat indicates that it is premature to unite these factors into a single variable for at least three reasons. First, non-critical and critical threats, as cognitive stimuli, cause different psychological reactions: the former leads to a more cognitive-based “discomfort,” and a problemistic search and consideration of alternatives (in line with the reasoning of Cyert and March 1963), whereas the latter causes more emotivebased stress and anxiety, forcing the decision makers to instead focus on staying within a known comfort zone (in line with threatrigidity thesis of Staw, Sandelands, and Dutton 1981). Second, these reactions are not mutually exclusive, and can act simultaneously mitigating one another (for example, non-critical threat driving the need for change simultaneously with critical threat causing rigidity). Finally, critical threat, or the prospect of business cessation, can be caused by other factors unrelated to anticipated losses, such as disputes among shareholders of the family firm. Performance Shortfall. In his seminal 1959 article Herbert Simon stated that as a conse-

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quence of performance shortfall, or the condition when a company’s performance does not meet aspiration level, “search behavior (particularly search for new alternatives of action) is induced” (p. 263). This thesis was elaborated further in the behavioral theory of the firm (Cyert and March 1963), showing that performance shortfall causes managers to initiate a problem-solving search (usually aimed at incremental fixes). On the other hand, meeting and exceeding the target performance level causes managers to be risk-averse and overconfident in their abilities; they relax instead of further looking for improvements. The phenomenon of avoiding strategic changes when exceeding the aspired performance level resulted in the moniker, “the fat cat syndrome” by Hedberg, Nystrom, and Starbuck (1976). The fat cat syndrome was identified in further studies of strategic decision making in large organizations (Amason and Mooney 2008; Greve 1998, 2008), as well as in entrepreneurial ventures (Mullins 1996). Drawing upon prospect theory (Kahneman and Tversky 1979), Fiegenbaum, Hart, and Schendel (1996) reached similar conclusions that strategic choice is risk-averse when a firm perceives itself above a pre-set reference point (aspiration level), and risktaking when performance shortfall occurs. Therefore, we hypothesize that performance shortfall will be positively associated with the risky intention to change the firm’s business model: H2: Performance shortfall is positively associated with intention to change the firm’s business model. Non-Critical Threat. The psychological explanation of active reaction to non-critical threat is provided by prospect theory (Kahneman and Tversky 1979), claiming, among other things, that decision makers show tendency for risk aversion when facing potential gains, and for risk seeking when facing potential losses. Since any change, particularly change in a business model, is perceived as a risky endeavor, this risk, unless mitigated, prevents managers from conceiving such changes. Prospect theory predicts that non-critical threats associated with future losses cause managers to be riskseeking, overcoming the risk-based obstacles to change, supporting the person in making a strategic decision to act. This argument is consistent with findings of Bowman (1982),


revealing the risk-seeking tendency of troubled firms. Finally, non-critical threat may trigger the small firm’s problematic search in line with predictions of behavioral theory of the firm (Cyert and March 1963). Hence, we hypothesize that the perception of non-critical threat causes intention to change the firm’s business model: H3: Perceived non-critical threat is positively associated with intention to change the firm’s business model. Critical Threat. Whereas non-critical threat pushes decision makers to act, even though it is out of their comfort zone, critical threat related to enormous losses that can lead to going out of business causes risk-averse response in the form of almost complete cessation of any actions. This situation is explained by threatrigidity thesis (Staw, Sandelands, and Dutton 1981), which shows that in the face of severe economic adversity organizational decision makers lose their ability to adapt. Due to stress and anxiety, their ability to process information and reason rationally significantly decreases; as a result, they limit their alternatives search to only familiar solutions, or do not act at all (becoming “rigid”). One of these familiar strategies, which involves minimal risk and does not require searching for alternatives, is increasing efficiency of available resources, causing “resource conservation” and limiting any actions aimed at obtaining the new resources or investing to develop the existing ones. We argue that small business owners are specifically vulnerable to threat-rigidity behavior in the face of a critical threat, in that losing their business—usually the main source of a family’s income—has enormous consequences for them, causing stress and anxiety serving the drivers of rigidity mechanism. Therefore, in line with threat-rigidity reasoning, we expect critical threat to impede strategic decisions to change the firm’s business model: H4: Perceived critical threat is negatively associated with intention to change the firm’s business model. It is worth noting that the provided above hypothesizing of rigid risk-averse response to severe adversity, stemming from the threatrigidity thesis (Staw, Sandelands, and Dutton

1981) can be challenged by the proponents of the prospect theory, predicting the risk-seeking innovative behavior of managers when perceiving potential losses (threat) regardless of the magnitude of this cognitive stimulus. To date, the conflict between the two perspectives (i.e., whether companies become more or less innovative in the situation of a major adversity— critical threat) remains unresolved; however, most recent studies favor—theoretically and empirically—the threat-rigid approach (Hu, Blettner, and Bettis 2011; Shimizu 2007). Therefore, we concur with the paper of Hu, Blettner, and Bettis (2011), stating that “while recognizing the mixed nature of the evidence, we believe the weight of the evidence and threatrigidity theory favor decreased risk taking in the vicinity of the survival point” (p. 1472). Hence, the negative sign of predicted association in H4.

Urgency and Environmental Dynamism The qualitative study by Lucas, Vermeulen, and Curseu (2008) reveals that feeling the need to act or change was in the list of motives for entrepreneurs making a strategic decision. We argue that this stimulus should be linked with the time dimension, anticipating that the perceived urgency of the need to act will serve as a driver for the entrepreneur to make the decision to change. Temporal motivation theory (Steel and Konig 2006) explains this phenomenon, claiming that the expected utility of an action is inversely related to perceived delay. Finally, the widely known John Kotter’s 8-step change model explicitly requires creating the sense of urgency to be the first step in any major strategic organizational change (Kotter and Cohen 2002). Therefore, we hypothesize that: H5: Urgency is positively associated with intention to change the firm’s business model. Environmental dynamism, or perceived pace of change of external environment, can also induce or impede strategic decision making. Similarly to perceived urgency, the environmental dynamism imposes a time pressure on decision makers, although now this pressure comes from the outside forces and do not necessarily require a response. Turbulent business environments cause small business managers to make fast decisions, using heuristics and intuition as shortcuts in reasoning, whereas stable


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environments may provide time for elaboration. Following Dewald and Bowen (2010), we argue that entrepreneur’s anticipated time of significant change of the business environment influences her intentions to alter the business model. This construct is different from urgency, though, in that environmental dynamism captures the perceived external pressures from the environment, whereas urgency captures the decision maker’s internal need to act. Obviously, environmental dynamism affects internal need for response; however, these perceptional factors can be influenced by different stimuli. For example, the need for immediate response (urgency) can be caused by a new opportunity that has nothing to do with environmental dynamism, and may induce actions regardless of the pace of change of external environment. Therefore, H6: Environmental dynamism is positively associated with intention to change the firm’s business model.

The Role of Successful Risk Experience Small business managers are different in their backgrounds; one of the salient differences among them stems from the prior experience of acting in high-risk and high-stake situations, as well as from the perceived results of these actions. Positive risk experience can drive up a person’s belief in her own abilities and judgment, increasing by this means overconfidence and representativeness biases, which were found to have an effect on an entrepreneur’s innovation activity (Curseu and Louwers 2008). Pablo (1997) showed that prior risk outcome history determines individual’s risk propensity, with positive risk experience reinforcing risk propensity driving future decisions. Positive risk experience can impede a manager’s risk aversion, which prevents change in a business model, particularly investment in resource development (Sauner-Leroy 2004). Since altering a business model is a risky high-stake decision, high risk propensity would support adoption. The hypothesized relationship between prior risk experience and intentions to adopt a new business model was empirically supported in Dewald and Bowen (2010). Hence, H7: Prior positive risk experience is positively associated with intention to change the firm’s business model.

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Inducements and Impediments to Opportunity Chasing One of the goals of this study is to show that opportunity perception per se is not the only factor causing strategic change. Furthermore, we argue that even the fact that opportunity is perceived does not automatically lead to emergence of an intention to change. For starters, it is reasonable to anticipate the interaction of recognized opportunity with the small business manager’s evaluation of situation’s threat and business’s performance. According to prospect theory (Kahneman and Tversky 1979), in the area of underperformance or non-critical threat an individual will be looking for solutions to correct the situation, and will pursue recognized opportunities more enthusiastically. On the other hand, when operating above aspirations, potentially safe from threat, a small business manager may be led by “the fat cat syndrome,” not enacting the opportunities, even though the latter are obvious. Moreover, the state of reaching the projected performance can affect an entrepreneur’s motivation: for instance, managers-owners of a family business might be willing to get a certain profit, and after reaching this point they relax and decide not to pursue risky endeavors any more. Finally, the high level of perceived critical threat may trump the intention to chase the opportunity because of the threat-rigidity mechanism (Staw, Sandelands, and Dutton 1981). Here we have to acknowledge again that this reasoning is not consistent with traditional prospect theory considerations, which predict risk-seeking and innovativeness regardless of the distinction between threat and critical threat. Nevertheless, the major body of current literature argues in favor of threat-rigid response to critical threat (Hu, Blettner, and Bettis 2011). Hence, we expect that: H8a: The relationship between perceived opportunity and intention to change the business model is positively moderated by performance shortfall, such that low level of performance shortfall decreases the likelihood of intention to change in reaction to perceived opportunity. H8b: The relationship between perceived opportunity and intention to change the business model is positively moderated by non-critical threat, such that low level of


non-critical threat decreases the likelihood of intention to change in reaction to perceived opportunity.

the effect of opportunity perception on intention to adopt the disruptive business model. Therefore:

H8c: The relationship between perceived opportunity and intention to change the business model is negatively moderated by critical threat, such that high level of critical threat decreases the likelihood of intention to change in reaction to perceived opportunity.

H10: The relationship between perceived opportunity and intention to change the business model is positively moderated by prior positive risk experience, such that low level of prior positive risk experience decreases the likelihood of intention to change in reaction to perceived opportunity.

Continuing the discussion of potential inducements and impediments for chasing the perceived opportunity, it is reasonable to hypothesize that the time pressure factors, namely urgency and environmental dynamism, can be substantive determinants of the process. Imposing a time pressure on the decision maker, urgency and environmental dynamism prevent elaborate analysis, forcing the manager to rely on heuristics and intuition as shortcuts in reasoning, resulting in reinforcement of intention to exploit the perceived opportunity. Furthermore, the time pressure factors reduce the perceived delay between the action (innovating to exploit the opportunity) and the reward for this action, by this means substantively increasing the action’s perceived expected utility (Steel and Konig 2006). Therefore: H9a: The relationship between perceived opportunity and intention to change the business model is positively moderated by urgency, such that low level of urgency decreases the likelihood of intention to change in reaction to perceived opportunity. H9b: The relationship between perceived opportunity and intention to change the business model is positively moderated by environmental dynamism, such that low level of environmental dynamism decreases the likelihood of intention to change in reaction to perceived opportunity. Finally, we hypothesize that the prior positive risk experience has both main and interacting effects on intention to change the business model. Decision makers who experienced successful risk outcomes in the past become more inclined to repeat risky decisions, including innovation in response to perceived opportunity. Following this line of reasoning, the study of Dewald and Bowen (2010) found that prior successful risk experience reinforces

Research Method Research Setting: Canadian Real Estate Brokerage Industry To effectively test our hypotheses, we sought an industry predominantly associated with small firms facing a pending disruptive change. A good example is the real estate brokerage industry in North America. The industry is dominated by entrepreneurial small firms run by managers or owner-managers (Zietz and Sirmans 2011), and faces major changes caused by network and technology-based disruptive innovations that could have a profound impact on business, including the possibility of disintermediation. However, unlike other intermediaries such as travel agencies and stockbrokers, for over a decade real estate brokers have largely avoided the impact of disruptive Internet-based change for two reasons: maintaining a monopoly over multiple listing services (MLS) and regulations that mandate minimal service standards. Nevertheless, the pressures to change created opportunities for disruptive innovations; particularly, during the last decade we observe emergence of disruptive technology (such as Internet sites listing property for sale) and disruptive business models (discounted brokers, mere posting service, services for FSBO-“For Sale by Owner”). More recently, several legal rulings have struck down the minimum service standards, and the Competition Bureau of Canada has taken a direct and forceful position to ensure open access and diversity of offerings are available to real estate consumers (Ladurantaye 2011a, 2011b). As anticipated, the traditional real estate brokers in North America feel the situation’s threat (Ladurantaye 2011a); some try to adapt new business models (Roberts 2011), whereas others stubbornly resist (Ladurantaye 2011b; Roberts 2011). Even though research shows that realtors still have an array of value-added


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services to propose to their customers besides access to MLS (Saber and Messinger 2010), and that including additional services to broker’s offer and emphasizing them improves revenues and net profit (Benjamin et al. 2008), evolving into or adopting a new innovative business model is risky (Roberts 2011).

Data Collection Method The primary object of the research (association between perceived characteristics of current situation and intended actions) can only be observed on the level of individual entrepreneur’s cognition. Therefore, we must take our measurements by direct inquiry through self-report measures. Following the example of Dewald and Bowen (2010), we developed and employed a survey instrument, using wherever possible the scales with tested reliability and validity found in existing literature. An invitation to fill in the electronic survey was sent by real estate brokerage regulatory authorities of two Canadian provinces, Alberta and British Columbia, to their members (brokers) via e-mail in May/2011. The survey was targeted at real estate brokers (generally owner-managers of the business), and participation was voluntary and anonymous. Overall, we received 288 submitted electronic surveys; of them 266 had at least one answer. 154 participants indicated that they operate in Alberta, a result revealing approximately 15 percent response rate for the province (similar to result reported by Dewald and Bowen 2010). To control for non-response bias, we used general information about our final sample’s participants to compare with characteristics of participants of similar studies of real estate brokerage industry (AREA 2004; Dewald and Bowen 2010). The sample is similar to prior studies in terms of all crucial characteristics, such as respondents’ gender (75 percent males), urban/rural clientele split (7 to 1), independent brokers versus franchise operators and corporate brokers (65, 33 and 9 percent respectively1). After deleting 25 observations with substantive proportion of missing values (when none of the dependent variables could be estimated), we obtained the final sample with 241 cases to be used in all further analyses. Occasional missing values in the final sample were

replaced using conservative mean substitution approach (Tabachnik and Fidell 2007).

Measures Our study employed 15 variables (3 criteria, 7 predictors, and 5 control variables); measures of constructs are described in Appendix 1. All variables were based on self-report measures, using 5-point Likert-type scales with neutral central point. Most of the variables were measured using multiple-item scales, with reliability of composite scale controlled using Cronbach’s α statistics. Alpha levels for each composite scale was found to be higher than acceptable threshold of .7 (Nunnally 1978), except for intended change in value structure, which nonetheless remains close to the cut-off value (α = .67). The resulting multi-item variables were calculated as sum of non-normalized values of individual items divided by the number of items; hence, the resulting composite scores vary between 1 and 5 with neutral certain point (3). Descriptive statistics and Pearson correlations for all employed variables are presented in Table 1. Confirmatory Factor Analysis. To validate the measurement model employed in the study, a SEM confirmatory factor analysis was conducted. The results of confirmatory factor analysis revealed appropriateness of used construct measures: overall factor model fitted the data reasonably well (χ2 = 579.92, df = 307, p = 0.0, χ2/df = 1.89, RMSEA = 0.066, NFI = 0.897, Non-Normed Fit Index (NNFI) = 0.936, CFI = 0.948, RMR = 0.075). All latent constructs measured using multiple-item scales had statistically significant factor loadings at .05 level, and all standardized loading exceeded .55 (well above the substantiveness threshold of .4), with majority being in >.75 range. Dependent Variables. We captured the strategic decisions made by real estate brokers using scales developed for this study, measuring intentions to change one or more of the three mutually-complementary dimensions of business model: resource, transactive and value structures. Intended change in resource structure (α = .764) captured the participant’s decisions to alter in any way the business’s resource

1

The sum exceeds 100, since respondents were able to choose more than one answer.

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Intended Change in Resource Structure Intended Change in Transactive structure Intended Change in Value Structure Age Gender (Dummy) Years in Brokerage Business Other Industries Experience (Dummy) Number of Employees (Log) Perceived Non-Critical Threat Perceived Critical Threat Perceived Opportunity Performance Shortfall Prior Positive Risk Experience Urgency Environmental Dynamism Negative Affectivity Social Desirability

S.D., standard deviation. N = 241. *p (two-tailed) < .05. **p (two-tailed) < .01.

17.

16.

14. 15.

13.

12.

11.

10.

9.

8.

7.

4. 5. 6.

3.

2.

1.

Variable

4.02

1.48

3.65 2.51

0.67

0.66

1.02 1.01

1.03

0.76

3.18

3.40

0.81

0.89

1.05

1.50

0.48

8.67 0.41 11.98

0.81

0.94

0.79

S.D.

3.74

2.20

2.97

2.40

0.52

55.51 0.23 19.66

3.75

3.01

4.01

Mean

4.02

1.20

4.00 3.00

3.40

3.00

4.00

2.00

3.00

2.40

0.52

55.51 0.00 19.66

4.00

3.00

4.00

Median

.097 −.018

−.068

.183** .228**

.112

−.100

.243** .109

.123

.115

−.167**

.115 .167**

−.187** .201**

.256**

−.021

.111

.210** .205**

.150*

.077

.122

.046

.089

.110

.191** −.096 −.079

.023

.128*

−.030

−.100 −.014 −.116

3

−.001 −.079 −.106

.599**

2

−.154* −.094 −.134

.457**

.252**

1

5

.053

.020

.010 .100

.025

.124 .029

.012

−.127*

.005

.008

.055

−.224**

6

.040

.066

.108

−.013

−.092 −.067 .141* .019

.009

-.018

.041 −.054

.171**

.025

−.082

−.110

−.131*

.081

.052

.164*

−.095 .581** −.048

4

8

.002

−.050

.099

−.020 −.035

−.014 .057 .010 −.105

.025

.091 −.074

.067 −.002

.037 −.102

.057 −.144**

.057

7

10

−.033

.375**

.183** .356**

11

.048

.444** −.237**

.195** .065 .278** −.079

−.092

12

.014 .012

13

.065

.028

.438** −.300**

.020 .072

.228** −.239**

.367** −.265** −.215** −.201**

.340**

−.263** −.412**

.720**

9

Table 1 Descriptive Statistics and Pearson Correlation Values

−.012

.061

.168**

14

−.154*

.269**

15

−.048

16

base through acquisition of tangible or intangible resources or building new expertise. Intended change in transactive structure (α = .700) aims at measuring the participant’s decisions to change the way business’s transactions, particularly with key external stakeholders—customers—are organized. Finally, intended change in value structure (α = .672) captures the entrepreneur’s plans to rethink the value proposition to customers, and to make appropriate changes in the way business is done. To assure that the construct of business model change has indeed three-facets structure with statistically differentiating three factors, we conducted two analyses. First, the Horn’s parallel analysis performed on all 9 indicators for changes in resource, transactive and value structures of the business model suggested the 3-dimensional structure of the solution (using the procedure by O’Connor 2000, run in Common Factor Analysis mode, with 1000 random datasets and 99 percentile taken into account). Second, in the SEM measurement model, fixing the correlation between any two latent constructs representing dependent variables to 1 (modeling them as a single factor) led to statistically significant and practically substantive worsening of the model fit: Δχ2(df=1) = 63.63, p < .001 for modeling resource and transactive structures as a single factor; Δχ2(df=1) = 47.12, p < .001 for resource and value strcutures; Δχ2(df=1) = 25.21, p < .001 for transactive and value structures. Independent Variables. We operationalized perceived opportunity (α = .786), non-critical threat (α = .837) and critical threat (α = .865) using scales from Dewald and Bowen (2010) as the basis, with new relevant questions added to increase reliability and to capture additional significant facets of constructs of interest. The difference between perception of non-critical threat and critical threat is captured through asking the questions about anticipated losses in revenues and profits (non-critical threat), as an opposite to survival of the whole business (critical threat). Since the two variables are, as expected, significantly correlated with each other (r = .72, p < .01), the additional checks were performed: In the SEM measurement model fixing the correlation between these latent variables to 1 led to substantive and statistically significant drop in the model fit (Δχ2(df=1) = 28.40, p < .001). Furthermore, the

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Horn’s parallel analysis performed on 8 indicators for critical and non-critical threats revealed the 2-dimensional structure of the solution (the procedure by O’Connor 2000, run in Common Factor Analysis mode, with 1000 random datasets and 99 percentile taken into account). The measure of performance shortfall (α = .909) was captured identically to Dewald and Bowen (2010). The highly reliable measure of positive risk experience (α = .936) was captured using scale proposed by Pablo (1997). Finally, urgency and environmental dynamism constructs were measured using single-item scales (similar to Dewald and Bowen 2010). Control Variables. Five control variables were added to the models to address the rival theories. Prior researchers argued that age is negatively related to strategic flexibility, supposing that young managers “would be less rigid in their cognitive frames due to a lack of institutionalized belief in the old business model” (Dewald and Bowen 2010, p. 209; see also Wiersema and Bantel 1992), or simply because of the difference in cognitive mechanisms of people of different ages (Parker 2006). The gender variable could capture possible gender differences in risk preferences (Langowitz and Minniti 2007; Yordanova and AlexandrovaBoshnakova 2011) or discovery of opportunities (Gonzalez-Alvarez and Solis-Rodriguez 2011). The number of employees variable (subject to the logarithm transformation to cure the substantive skewness of the distribution) is intended to capture the possible impediments to strategic change caused by organizational contextual factors correlated with its size, such as resource availability and inertia (Hannan and Freeman 1977), or institutional rules (DiMaggio and Powell 1983; Meyer and Rowan 1977). Finally, years in brokerage business and other industries experience variables, derived from research in top management teams demography, are intended to capture the influence of entrepreneur’s professional experience on her strategic decision making: Some authors argue that for the members of top management teams the tenure (years in brokerage) leads to cognitive rigidity, commitment to status quo and reluctance to make strategic changes (Finkelstein and Hambrick 1996; Wiersema and Bantel 1992), whereas the diversity of experience (e.g., obtained in other industries) improves cognitive flexibility (Harris and Helfat 1997; Wiersema and Bantel 1992).


Common Method Variance Bias. Selfresponse measures are vulnerable to a set of biases (Kline, Sulsky, and Rever-Moriyama 2000; Podsakoff and Organ 1986); the mechanisms for mitigating or controlling these biases were employed in our study through design procedures and post-factum statistical control. Particularly, the correlations analysis (Table 1) revealed that neither social desirability nor negative affectivity had a significant impact on dependent variables. Moreover, the postfactum Harman’s statistical test revealed absence of single factor simultaneously influencing the studied measures.

Results Regression Models We applied hierarchical ordinary least squares regression models (see Table 2) to test the hypothesized theoretical framework. In line with our theoretical argument, rethinking the value structure may lead to further alteration of the other two dimensions of the business model—in the cases of transformative change. Taking into account that the intended change in value structure of the business model was found significantly correlated with the two other dependent variables (see Table 1), to estimate the unique effects of hypothesized predictors on intentions to change the resource and transactive structures, we directly controlled for intended change in value structure in our models. By this means, we assured that the observed significant predictors of change in resource and transactive structures are not caused by the mediating effect of the value structure. On the first step (Model 1) we added the control variables (including intended change in value structure for the models of resource and transactive structures), on the second (Model 2)—variables for measuring hypothesized main effects, on the third (Model 3)—interaction terms for testing the hypothesized moderation effects. On the first step, aimed at analyzing the effect of control variables on intended changes in different facets of business model, the two regression models—for intended change in resource structure and transactive structures—were found statistically significant (F = 13.292, p < .01 and F = 26.970, p < .01, respectively). Predictably, in both models the intended change in value structure was a

statistically significant predictor with positive effect (β = .426, p < .01 for resource structure; β = .617, p < .01 for transactive structure). Moreover, on the first step in the two discussed models the control variable of number of employees (log) had statistically significant regression coefficient, albeit with different signs (β = .147, p < .05 for resource structure; β = −.176, p < .01 for transactive structure). The latter fact leads to a conclusion that the higher number of employees increases the likelihood of altering the resource structure of the business model, simultaneously decreasing the likelihood of rethinking the transactive structure. The second step, testing the hypothesized main effects of independent variables, yielded significant incremental improvement of all three regression models. Interestingly, none of H1 to H7 were either rejected or fully supported: even though each predictor was significantly associated with at least one dependent variable—intentions to alter one of the three facets of the business model—not a single predictor was associated with all three of them. In line with hypothesized predictions, perceived opportunity (H1) had significant positive effect on intentions to change transactive and value structures of the business model. Consistently with H2, performance shortfall was associated with intended change in resource and value structures, although for the former it had the negative sign—contrary to the prediction. Perceived non-critical threat (H3) had significant positive effect only on intended change in transitive structure. In line with H4, perceived critical threat had significant negative effect on resource structure. Taking into account the substantive correlation between perceived critical and noncritical threat (r = .72, p < .01), we performed additional robustness check to assure that the results for H3 and H4 are not distorted by common variance, which is not linked to either of the correlated predictors in OLS regression model—a fact leading to potential deteriorating of regression coefficients’ significance. In the check, we were adding noncritical and critical threat into all three regression models one at a time (in separate models each of them was added before the other one), to make sure that their common variance is accounted for. This procedure yielded similar results: non-critical threat was


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Intended Change in Transactive Structure

Intended Change in Value Structure

.254 13.292** .254 13.292**

−.093 −.081 .147* −.060 −.064 .426**

.464 15.095** .055 3.316**

.409 26.970** .409 26.970**

.224** −.126 −.008 −.068 .006 .063 .379 7.108** .041 2.458* .338 8.910** .084 4.098**

.125* .031 .258** −.089 .017 .038 .043

.042 −.130* .091 −.222* .156** .079 .004

.027 −.120* .064 −.226** .159** .083 .000

−.070 .079 −.134** .065 −.056 .558**

−.069 −.029 .084 −.061 −.114 .408**

−.080 −.034 .127* −.052 −.077 .391**

−.070 .081 −.176** .090 −.056 .617**

.133* .023 .026 .055 .064 .005 .490 11.192** .027 1.929†

.112† .048 .270** −.057 .025 .037 .066

−.072 .091 −.132* .058 −.079 .562**

.029 1.409 .029 1.409

−.014 −.057 .117† .004 −.089

.157 3.545** .128 4.952**

.117† .142* .045 −.003 .145* .194** .166*

−.023 −.111 .146* −.007 −.048

−.066 .101 −.033 .079 −.066 .060 .178 2.675** .021 .945

.124† .157* .015 .010 .153* .200** .166*

−.029 −.128 .148* −.006 −.024

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

Intended Change in Resource Structure

N = 241. All VIF are less than 3.6. †p (two-tailed) < .10; *p (two-tailed) < .05; **p (two-tailed) < .01.

Control Variables Gender (Dummy) Age Number of Employees (log) Other Industries Experience (Dummy) Years in Brokerage Business Intended Change in Value Structure Predictors Perceived Opportunity Performance Shortfall Perceived Non-Critical Threat Perceived Critical Threat Urgency Environmental Dynamism Prior Positive Risk Experience Interactions Opportunity × Performance Shortfall Opportunity × Non-Critical Threat Opportunity × Critical Threat Opportunity × Risk Experience Opportunity × Urgency Opportunity × Environmental Dynamism R-squared F Step’s R-squared Change Step’s F Change

Dependent Variable

Table 2 Regression Models—Standardized (β) Coefficients

associated only with change in transactive structure; critical threat—only in resource structure (see Appendix 2). The factor of urgency (H5) was positively associated with intended change in resource and value structures. Environmental dynamism (H6) and prior positive risk experience (H7) were significantly associated only with change in value structure.

The third step (Model 3) revealed support only for H8a: the interaction term of perceived opportunity and performance shortfall was significant and positive for intended change in resource and transactive structures. The graphical representation the interaction is shown on charts in Figure 1. All other hypothesized interactions (H8b, H8c, H9a, H9b, and H10) were found statistically

Figure 1 Moderation Effect of Performance Shortfall on the Relationships between Perceived Opportunity and Intended Change of Business Model. (a) Intended Change in Resource Structure. (b) Intended Change in Transactive Structure a

b


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insignificant.2 The summary of the hypothesis testing results is presented in Table 3.

Mediation Analysis To test whether, in line with the theoretical reasoning, the significant predictors of change in value structure (opportunity, performance shortfall, urgency, environmental dynamism, and prior positive risk experience) also had a significant effect on the remaining two facets of the business model (mediated through the value structure), an additional mediation analysis was performed. Following the modern approach to mediation analysis (Zhao, Lynch, and Chen 2010), we employed the PreacherHayes bootstrapping extension of the Sobel test (Preacher and Hayes 2010) to determine the indirect effects of the listed predictors on the change in resource and transactive structures as mediated by change in value structure. The analysis (Appendix 3) suggests that urgency and environmental dynamism have significant indirect effects on resource and transactive structures; prior positive risk experience—only on resource structure; whereas opportunity and performance shortfall had statistically insignificant indirect effect on either of the variables (since 95 percent confidence intervals for their Sobel’s coefficients—indirect effects—included zero).

Discussion and Implications We found strong support for the view that cognitive factors drive small business managers’ strategic decision making. Moreover, the decision maker’s framing of a situation and her prior experience combine to predict her response to changes in external environment, including the emergence of disruptive business models. The types of response can be accurately modeled onto intentions to change one or more dimensions of the three-dimensional view of a business model (George and Bock 2011), embracing resource, transactive, and value structures. Consistent with the main thread in entrepreneurship literature, we found that opportunity recognition is a driver for strategic change and that it induces change in all aspects of a small firm’s business model, either directly or in

interaction with other variables. Particularly, we found that the influence of perceived opportunity on the intentions to change in resource and transactive structures of the business model is not linear, and is moderated by the small business manager’s estimation of her own performance (see charts in Figure 1). Higher levels of performance impede the positive effect of perceived opportunity on change intentions. This phenomenon is the most salient with intentions to alter the resource structure (see Figure 1a): the slope of “opportunity-intended change” line falls down to zero (insignificant regression coefficient) when the business’s performance is above projections. The high level of line intercept in the latter case shows that satisfaction with performance leads to stable high level of investments into development of business’s resource base, regardless of the perception of the opportunity in the external environment’s change. As predicted, performance shortfall has a direct significant main effect on small business manager’s intentions to change the value structure of the business model, indicating that, in accordance with predictions of behavioral theory of the firm (Cyert and March 1963), not reaching the aspiration performance level triggers problemistic search. This search has transformative influence, and results in rethinking of business’s value proposition. Contrary to what was hypothesized, the impact of performance shortfall on the resource structure is significant and negative, arguably because underperformance leads to resource conservation, eliminating problematic search within this facet of the business model. Perceived non-critical threat triggers a search for solutions through change in the transactive structure. Lack of anticipated influence of perceived non-critical threat on intentions to change the value structure of a business model means that, unlike perceived opportunity, non-critical threat does not trigger profound transformative rethinking of a business model (embracing resource, transactive and value structure), limiting the strategic response to changes in the way operations are organized (incremental change, in transactive structure only). In other words, as a response

2 Additional robustness checks (eliminating the opportunity × performance shortfall interaction from the model, running the third step in a stepwise manner) did not reveal any additional interactions.

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Perceived opportunity Performance shortfall Perceived non-critical threat Perceived critical threat Urgency Environmental dynamism Prior positive risk experience Opportunity × Performance shortfall Opportunity × Non-critical threat Opportunity × Critical threat Opportunity × Urgency Opportunity × Environmental dynamism Opportunity × Risk experience

Predictor Intended Change in Transactive Structure + − + − −a −a − + − − − − −

Intended Change in Resource Structure − opposite sign − + +a −a −a + − − − − −

Dependent Variable

+ + − − + + + − − − − − −


The predictor had significant indirect effect on the dependent variable, mediated by intended change in value structure (see the mediation analysis, Appendix 3). “+” hypothesis supported; “−” hypothesis not supported.

a

H1 H2 H3 H4 H5 H6 H7 H8a H8b H8c H9a H9b H10

Hypothesis

Table 3 Summary of Hypothesis Testing Results

to non-critical threat entrepreneurs try to “patch” their business model instead of developing it. Critical threat triggers emotional threat rigidity mechanisms, as well as rational reluctance to invest in a business that has a reasonable chance of ceasing to exist—both mechanisms impeding the intentions to change the resource structure of the business model. Contrary to the predictions, perception of critical threat did not have any impact on the transactive and value structures of the business model, demonstrating that, unlike resource rigidity, the routine and value rigidities are not the standard response to major adversity. However, there is considerable plausibility in managerial focus on the resource structure when in an emotive threat-rigid situation. Essentially, this may reflect an impulsive “reaction” (versus a measured response) to not throw good money after bad. Resource structure investment is logically the first stage of retreat. As predicted, both urgency and environmental dynamism had significant effect on intentions to alter the business model—firstly on value structure, then—indirectly—on the two other facets. Hence, John Kotter’s famous first step in managing change (Kotter and Cohen 2002)—creating the sense of urgency— received empirical support. Finally, prior positive risk experience affected the intention to change the value structure of the business model, and—indirectly— the resource structure; this finding suggests that the experience of making changes in the past facilitates innovating in times of disruptive change. Of all the control variables, number of employees deserves special attention: this factor was positively associated with changes in resource structure and value structure, and negatively with changes in transactive structure; these results indicate that a typical reaction of larger companies to disruptive change is merely buying new resources (“patching”), instead of rethinking the whole business model—particularly its transactive structure (organization of operations, proposed products and services, efficiency of business processes). This phenomenon, however, can be peculiar to certain industries (e.g., mature ones), and requires further investigation. Concluding the results of this discussion, we stress that from all key cognitive factors, perception of opportunity causes entrepreneurial

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small business managers to profoundly rethink their whole business model (value, transactive and resource structures). Performance shortfall has somewhat lesser effect, inducing intentions to rethink value proposition, with no immediate intention to invest in development of the resource base, and non-critical threat introduces “patching” onto the business model through altering its transactive structure, without deep rethinking of the value proposition. This implies a preference for theories associated with entrepreneurial rather than behavioral responses to disruptive change.

Implications for Small Businesses To appreciate the implications for small business owners-managers, it is important to first understand that in the face of lowcost disruptive change, transformative change through the adoption of the new value model is the rational choice (Christensen and Raynor 2003). However, our findings confirm that rationality takes a back seat to powerful cognitive influences on strategic decision making. The barriers to rational response can be described as a series of cognitive layers. Our research confirms that as a first-level barrier, it is unlikely that a small business manager will choose any form of business model revision without first recognizing an opportunity. Unlike behavioral studies targeted mostly at larger businesses, for small firms, opportunity trumps performance shortfall and threat-grounded behavioral triggers. Hence, entrepreneurs need to resist the cognitive barriers and be open to and accepting of new opportunities to engage their search mechanism in the face of disruptive change. Opportunity in combination with performance shortfall will motivate transformative change. We also found that it is important for small business managers to develop a sense of urgency toward transformative change, which is consistent with change literature. Limitations and Future Research Our study has a set of limitations. First, as discussed earlier, we captured small business managers’ perceptual factors and strategic decisions made (measured through intentions to make changes in the business model) at a culminant moment in time, when the real estate brokerage industry faced salient disruption. In this study, we acknowledge ignorance concerning the implementation of these strategic


decisions. Our study could be enhanced by a dynamic research model taking into account the lag between the emergence of intentions and actual implementation of strategic actions. Further investigation is necessary to explore the relationship between the scrutinized in this study factors imposing the time pressure on the decision maker—perceived urgency and perceived environmental dynamism. Having different origins (internal versus external, respectively), these two factors are nevertheless linked together, with dynamism being a potential cause of urgency. Nevertheless, these two constructs are theoretically and empirically distinct, with a modest correlation of .168. We would encourage the other studies exploring the link between these constructs, in their influence on each other and on the decision making process. A weakness of this study’s measurement, leaving room for a potential for error, is in using single-item scales for capturing the important constructs of perceived urgency and environmental dynamism. Hence, we would encourage researchers to improve measurement of these constructs. Moreover, some of our constructs, being never operationalized in survey design before (such as change in three facets of the business model), need to be tested and—possibly—improved in the further studies. Particularly promising would be operationalizing intended change in resource structure of the business model with shifting emphasis from growth (as it is done in current paper) to reconfiguration of resources; further studies might find out that the reported results are biased because of the adopted growth imperative (i.e., low score on intended change in resource structure measured using proposed scale means low intention to grow, not to change through reconfiguration).

Conclusion In our study we developed and empirically tested a holistic model that measures the impact of small business managers’ cognition, particularly the perception of situation, performance and prior experience on intentions to alter the firm’s business model. Our model and results contribute to the growing body of research on opportunity recognition, exploitation, and entrepreneurial strategic decision making. Also, by testing the models in the context of real estate brokerage industry, which faces

current and potentially critical threats from competitive disruptive business models, we contribute to the literature on incumbent response to disruptive innovations. We emphasize the import contribution of George and Bock (2011)—the threedimensional view of the business model— having the potential to open up a whole new field of research within entrepreneurship, strategy, and small business management literature. By providing empirical evidence concerning important distinctions between factors that cause change in each of the resource, transactive, and value structures, the current paper can be leveraged by further studies of various factors influencing distinct aspects of business model change. Finally, the results provide not merely theoretical, but practical value: by exposing cognitive factors that explain the strategic decisions, we reveal biases that can constrain a small business manager’s decision making, pushing this process off from the optimal path.

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Appendix 1 Measures of Constructs in the Study Variable Name Dependent Variables Intended Change in Resource Structure



Independent Variables Perceived Non-Critical Threat

Perceived Critical Threat

Perceived Opportunity

Performance Shortfall

Measured Items

You will acquire (are acquiring now) new resources to grow your business (e.g., new software to help operations, new online listing system, etc.) You will provide (are providing) additional education to your employees (and/or yourself) to grow your business You will improve your IT systems to increase the efficiency of the operations You will change (have changed) the structure of the services you offer to clients (e.g., unbundling, menu of services) You will propose (are proposing) new services to clients (e.g., discounted access to MLS) To increase the efficiency of operations, you will change the emphasis among components of service you provide to customers (e.g., listing, searching, showing houses, negotiating, etc.) You will rethink the value and structure of services you propose to customers To deliver more value to customers, you will change the emphasis among components of service you provide to customers (e.g., listing, searching, showing houses, negotiating, etc.) You will make changes to your business to increase customer value

Very Unlikely (1)—Very Likely (5)

Your profits will be reduced due to discount brokerage (reduced commission or commission rebate) competitors The mere posting offerings will cause all real estate fees to decrease The mere posting offerings will cause lower margins for brokers Ongoing changes in regulation and the marketplace threaten the survival of your business The discount brokerage model will replace conventional real estate brokers The mere posting offerings will eliminate the role of conventional real estate brokers New technologies (Internet, social media, etc.) will eliminate the role of conventional real estate brokers In the future, there will be no role for conventional real estate brokers Consumer home search on the Internet is an opportunity for your business to grow New brokerage models present new opportunities for you Technology (tablets, e-transactions, social media, etc.) presents an opportunity for your business’s growth Your transaction volume in the current year will be

Strongly Disagree (1)—Strongly Agree (5)

Your profit in the current year will be Your profit per transaction in the current year will be Prior Positive Risk Experience

Urgency Environmental Dynamism Control Variables Number of Employees

Think back to a significant business situation in the past when you took the more risky alternative: How pleased were you with the outcome? Overall how would you rate the outcome? How would you classify the result? The changes in real estate business environment require immediate response from you* Regulation changes will lead to significant changes in how the real estate brokerage industry operates over the next*

What is the number of your employees (sales agents, managers, brokers; excluding clerical staff) Age Please indicate your age Gender Please indicate your gender Years in Brokerage Business How many years have you been in brokerage business? Other Industries Experience Did you have substantive (15 years+) experience in other industries prior to becoming a real estate broker?

α

Anchors

.764

Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5)

.700

Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5)


.672


Very Unlikely (1)—Very Likely (5) .837

Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5) Strongly Disagree (1)—Strongly Agree (5)

.865

Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5) Very Unlikely (1)—Very Likely (5) Strongly Disagree (1)—Strongly Agree (5)

.786

Strongly Disagree (1)—Strongly Agree (5) Strongly Disagree (1)—Strongly Agree (5) Well Above Projections (1)—Well Below Projections (5) Well Above Projections (1)—Well Below Projections (5) Well Above Projections (1)—Well Below Projections (5)

.909

.936

Not At All (1)—Totally (5) Very Negative (1)-Very Positive (5) Complete Failure (1)-Complete Success (5) Definitely True (1)—Definitely False (5) 6 months (1), a year (2), 3 years (3), 5 years (4), more than 5 years (5) Open-ended Open-ended Male (0), Female (1) Open-ended Yes (1), No (0)

*Resulting construct employs reversed item.


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Appendix 2 Robustness Check: Non-Critical versus Critical Threat in Regression Models Regression Models—Standardized (β) Coefficients Dependent Variable

Intended Change in Resource Structure



Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Non-Critical Critical Both Non-Critical Critical Both Non-Critical Critical Both Threat Threat Threat Threat Threat Threat Only Only Only Only Only Only Control Variables Gender (Dummy) Age Number of employees (log) Other Industries Experience (Dummy) Years in Brokerage Business Intended Change in Value Structure Predictors Perceived Opportunity Performance Shortfall Urgency Environmental Dynamism Prior Positive Risk Experience Perceived Non-Critical Threat Perceived Critical Threat R2 F Model 3 versus Model 1: ΔR2 Incremental F Model 3 versus Model 2: ΔR2 Incremental F

−.086 −.070 .130* −.043

−.082 −.041 .123* −.048

−.080 −.034 .127* −.052

−.072 .065 −.133** .069

−.075 .051 −.150** .082

−.070 .079 −.134** .065

−.023 −.112 .146* −.007

−.024 −.116 .143* −.005

−.023 −.111 .146* −.007

−.049 .392**

−.071 .393**

−.077 .391**

−.045 .558**

−.032 .564**

−.056 .558**

−.048

−.044

−.048

.078 −.139* .139* .081 .000 −.071

.030 −.115† .161** .094 −.006

.027 −.120* .159** .083 .000 .064 −.226** .338 8.910**

.145** .023 .009 .037 .043 .205**

.137* .052 .025 .082 .021

.125* .031 .017 .038 .043 .258** −.089 .464 15.095**

.318 8.846**

−.184** .336 9.618**

.020 6.920**

.461 16.221**

.079 .435 14.636**

.118† .142* .145* .194** .166* .043 .157 3.884**

.120† .146* .146* .202** .163* .026 .156 3.859**

.117† .142* .145* .194** .166* .045 −.003 .157 3.545**